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LAG-YOLO:Efficient road damage detector via lightweight attention ghost module

Junxin Chen Xiaojie Yu Qiankun Li Wei Wang Ben-Guo He

智能建造学报（英文）2024，Vol.2Issue(1)：30-39,10.

智能建造学报（英文）2024，Vol.2Issue(1)：30-39,10.DOI:10.26599/JIC.2023.9180032

LAG-YOLO:Efficient road damage detector via lightweight attention ghost module

Junxin Chen ¹Xiaojie Yu ¹Qiankun Li ²Wei Wang ³Ben-Guo He⁴

作者信息

1. School of Software,Dalian University of Technology,Dalian 116621,China
2. Department of Automation,University of Science and Technology of China,Hefei 230027,China
3. Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing,Artificial Intelligence Research Institute,Shenzhen MSU-BIT University,Shenzhen 518038,China
4. Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines,Northeastern University,Shenyang 110819,China
折叠

摘要

关键词

road damage/deep learning/lightweight detector/attention mechanism

Key words

road damage/deep learning/lightweight detector/attention mechanism

引用本文复制引用

Junxin Chen,Xiaojie Yu,Qiankun Li,Wei Wang,Ben-Guo He..LAG-YOLO:Efficient road damage detector via lightweight attention ghost module[J].智能建造学报（英文）,2024,2(1):30-39,10.

基金项目

This work is funded by the National Natural Science Foundation of China (Nos. 52222810 and 62171114)and the Fundamental Research Funds for the Central Universities (No. DUT22RC(3)099). （Nos. 52222810 and 62171114）

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